Gozzi Nicolò, Perra Nicola, Vespignani Alessandro
Institute for Scientific Interchange Foundation, Turin 10126, Italy.
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115.
Proc Natl Acad Sci U S A. 2025 Jun 17;122(24):e2421993122. doi: 10.1073/pnas.2421993122. Epub 2025 Jun 12.
Characterizing the feedback linking human behavior and the transmission of infectious diseases (i.e., behavioral changes) remains a significant challenge in computational and mathematical epidemiology. Existing behavioral epidemic models often lack real-world data calibration and cross-model performance evaluation in both retrospective analysis and forecasting. In this study, we systematically compare the performance of three mechanistic behavioral epidemic models across nine geographies and two modeling tasks during the first wave of COVID-19, using various metrics. The first model, a Data-Driven Behavioral Feedback Model, incorporates behavioral changes by leveraging mobility data to capture variations in contact patterns. The second and third models are Analytical Behavioral Feedback Models, which simulate the feedback loop either through the explicit representation of different behavioral compartments within the population or by utilizing an effective nonlinear force of infection. Our results do not identify a single best model overall, as performance varies based on factors such as data availability, data quality, and the choice of performance metrics. While the Data-Driven Behavioral Feedback Model incorporates substantial real-time behavioral information, the Analytical Compartmental Behavioral Feedback Model often demonstrates superior or equivalent performance in both retrospective fitting and out-of-sample forecasts. Overall, our work offers guidance for future approaches and methodologies to better integrate behavioral changes into the modeling and projection of epidemic dynamics.
描述将人类行为与传染病传播（即行为变化）联系起来的反馈，在计算和数学流行病学中仍然是一项重大挑战。现有的行为流行模型在回顾性分析和预测中往往缺乏实际数据校准和跨模型性能评估。在本研究中，我们使用各种指标，系统地比较了三种机制性行为流行模型在九个地区和两项建模任务中，在新冠疫情第一波期间的表现。第一个模型是数据驱动的行为反馈模型，它通过利用移动性数据来捕捉接触模式的变化，从而纳入行为变化。第二个和第三个模型是分析性行为反馈模型，它们通过明确表示人群中的不同行为类别，或利用有效的非线性感染力来模拟反馈回路。我们的结果并未确定一个总体上最佳的单一模型，因为性能会因数据可用性、数据质量和性能指标的选择等因素而有所不同。虽然数据驱动的行为反馈模型纳入了大量实时行为信息，但分析性分类行为反馈模型在回顾性拟合和样本外预测中往往表现出更好或相当的性能。总体而言，我们的工作为未来更好地将行为变化纳入疫情动态建模和预测的方法提供了指导。
